The most important message for Japan is that
the overall level of prices associated with GDP is back to 1980 and the long
term fall will continue into the next few decades.

By trial-and-error, we seek for the
best-fit coefficients in the linear and lagged link between inflation and labour
force. Because of the structural (measurement related) break in the 1980s, we
have chosen the period after 1981 for linear regression, which is common for
almost all economic studies related to Japan. By varying the lag and coefficients
we have found the following relationship:

p(t) = 1.9dLF(t-t0)/LF(t-t0) – 0.0084(1)

where the time lag t0=0 years; Figure 1depicts
this best-fit case. There is no time lag between the inflation series and the
labour force change series in Japan. Free term in (1), defining the level of price
inflation in the absence of labour force change, is close to zero but negative.

A more precise and reliable representation
of the observed and predicted inflation consists in the comparison of
cumulative curves shown in the lower panel of Figure 1. We always stress that
the cumulative values of price inflation and the change in labour force are the
levels of price and labour force, respectively. Therefore, the summation of the
annual reading gives the original estimates of price and workforce, which when are
converted into rates.

Another advantage of the cumulative
curves is that all short-term oscillations and uncorrelated noise in data as induced
by inaccurate measurements and the inevitable bias in all definitions are
effectively smoothed out. Any actual deviation between these two cumulative
curves persists in time if measured values are not matched by the defining
relationship. The predicted cumulative values are very sensitive to free term
in (1).

For Japan, the DGDP cumulative curves
are characterized by very complex and unusual for economics shapes. There was a
period of intensive inflation growth and a long deflationary period. The labour
force change, defining the predicted inflation curve, follows all the turns in
the measured cumulative inflation with the coefficient of determination R2=0.97 (R2=0.77
for the annual estimates). (Again, these are actually measured curves.) With
shrinking population, and thus, labour force, the GDP deflator will be falling
through 2050 and likely beyond.

Figure
1. Measured GDP deflator and that predicted from the change rate of labour
force in Japan. Upper panel: Annual curves smoothed with MA(3). Lower panel: Cumulative curves between
1981 and 2012. The extremely accurate agreement between the cumulative curves
illustrates the predictive power of our model.

Two years ago we wrote a paper on price inflation and
unemployment in Australia. It allowed us making a projection into 2050:

“As a final remark on the evolution inflation (DGDP)
and unemployment in Australia we present two predictions as based on the labour
force projection provided by the Productivity Commission (2005) and the
coefficients in (7) and (8) estimated for the period after 1994: a1=3.299, a2=-0.0259; b1=-2.08, b2=0.0979. We assume that
there will be no change in the definitions of all involved macroeconomic
variables through 2050 and these coefficients will hold.Unfortunately, the accuracy of labour force
projection has a poor historical record, taking into account the projection
between 1999 and 2016. Nevertheless, it may be useful for assessment of the
long-term evolution.Figure 15 displays
both predictions, with the period before 2010 represented by actual labour
force measurements since the projected ones were not accurate.

Figure 15. Prediction of
inflation and unemployment in Australia through 2050 as based on the labour
force projection provided by the Productivity Commission.

The
level of price inflation after 2015 will likely fall below zero and will remain
at -1.5% per year through 2050. This lengthy period of deflation will be
accompanied by an elevated rate of unemployment approaching 9% around 2030. The
evolution of both variables is not fortunate for the Australian economy and is
chiefly associated with the population ageing. The latter suppresses
demographic growth and reduces the rate of participation in labour force.
Australia will likely need a larger international migration to overcome deflation
and high unemployment. This is the means to overcome deflation the U.S. has
been using for many years, but even with a large positive migration the
Australian economy will be on the brink of deflation during the next four
decades. Without migration, Australia will soon join Japan having the same
demographic problems and price deflation since the late 1990s.”

Here, we update our projections with three new readings
for 2010 through 2012. (We have borrowed all estimates from the Australia
Bureau of Statistics.) The Australian economy is heading into a long deflation
period with an elevated unemployment. In 2013, the rate of unemployment will
rise to 6.5% or even 7.0%. The GDP deflator will likely be negative in 2013 following
the 2012 trend.

The reason behind these processes is the same as in Japan – the fall
in labor force.

7/23/13

The Fifth Meeting of the Society for the Study of Economic
Inequality (ECINEQ) will be held at the
University of Bari (Italy) from July 22 to July 24,
2013.

The ECINEQ
conference provides a forum for a rigorous analysis of inequality, welfare and
redistribution issues, both at the theoretical and at the empirical level, as
well as for a discussion of the policy implications of the research findings in
this field.

ECINEQ aims at achieving high scholarly
standards in both the selection of topics and their debates, whether they
concern theoretical issues, empirical analyses or the implementation of
policies.

We model the evolution of age-dependent personal money income distribution and income inequality as expressed by the Gini ratio. In our framework, inequality is an emergent property of a theoretical model we develop for the dynamics of the individual income growth with age. The model relates the evolution of personal income to the individual’s capability to earn money, the size of her work instrument, her work experience and aggregate output growth. Our model is calibrated to the single-year population cohorts as well as the personal incomes data in 10-and 5- year age bins provided by the Census Bureau. We predict the dynamics of personal incomes for every single person in the working-age population in the USA between 1930 and 2011. The model output is then aggregated to construct annual age-dependent and overall personal income distributions (PID) and to compute the Gini ratios. The latter are predicted very accurately - up to 3 decimal places. We show that Gini for people with income is approximately constant since 1930, which is confirmed empirically. Because of the increasing proportion of people with income between 1947 and 1999, the overall Gini reveals a tendency to decline slightly with time. The age-dependent Gini ratios have different trends. For example, the group between 55 and 64 years of age does not demonstrate any decline in the Gini ratio since 2000. In the youngest age group (from 15 to 24 years), however, the level of income inequality increases with time. We also find that in the latter cohort the average income decreases relatively to the age group with the highest mean income. Consequently, each year it is becoming progressively harder for young people to earn a proportional share of the overall income.

7/21/13

There is an
eternal fight between economics and science. One of the most active fronts that
economics holds against scientific knowledge and even common sense is data. Behind
this front, in the realm of economics, the soldiers and commanders of economic
knowledge commit suicide. Every time, when they use own data.

For a physicist,
high data quality is a must. Economists revise their estimates at a high rate
and deliberately make them incompatible over time. This is a suicide. Today, I
ran across a dramatic update to the Total Economy Database (TED) maintained by
the Conference Board. I use this database extensively and always considered it
as a reliable source of macroeconomic estimates. Before today.

So, what is the
problem? When modeling labor productivity in developed countries I used the Geary-Khamis
estimates expressed in 1990 US dollars. The data gave excellent results
reported in this blog and a few papers (1, 2,
3). For Turkey, I presented the following figure
in 2010:

Figure 1.
Comparison of the measured and predicted labor force productivity in Turkey based
on the 2010 Total Economy Database.

Today, I tried
to update the previous model using the 2013 version of TED and found the following
pattern:

Figure 2.
Comparison of measured and predicted labor force productivity in Turkey based
on the 2013 Total Economy Database.

What the …? – was my first thought. Has the model failed?
The second thought was more creative – Does the economics profession continue
its war on data? And this was a correct assumption. Figure 3 shows that the 2013 TED contains a $2500
step (~15%!) in 2003 without any change in real GDP per capita in the very same
year. I found that weird and checked some other countries. Figure 4 shows that
in some cases the revision to the labor force productivity estimates was really
dramatic.

How dare economists claim that their theories should
not be corroborated by data? They slaughter data every day with a big rusty
knife of insane revisions.

Figure 3. The difference
between the 2013 and 2010 versions of the TED for labor force productivity (LP)
and GDP per capita in Turkey.

Figure 4. The difference
between the 2013 and 2010 versions of the TED for labor force productivity in
selected developed countries.

7/20/13

The growth in labor productivity, P, is the driver of real economic
growth. Since 1970, the growth rate, dP/P, in Belgium was on a falling trend. We published
two papers [1,
2] five years
ago. Figure 3 from paper [2] is reproduced below. Our prediction was that the rate of labor
force growth would fall below zero. Here we revisit this prediction and provide
a new projection 5 years ahead. We begin with the model, which is also
described in both papers

For the estimation of labor
productivity one needs to know total output (GDP) and the level of employment, E (P=GDP/E),
or total number of working hours, H (P=GDP/H). In the first approximation and for the purposes of our modeling,
we neglect the difference between the employment and the level of labor force
because the number of unemployed is only a small portion of the labor force.
There is no principal difficulty, however, in the subtraction of the
unemployment, which is completely defined by the level of labor force with
possible complication in some countries induced by time lags. The number of
working hours is an independent measure of the workforce. Employed people do
not have the same amount of working hours. Therefore, the number of working
hours may change without any change in the level of employment and vice versa. In
this study, the estimates associated with H
are not used.

Individual productivity
varies in a wide range in developed economies. In order to obtain a
hypothetical true value of average labor productivity one needs to sum up
individual productivity of each and every employed person with corresponding
working time. This definition allows a proper correction when one unit of labor
is added or subtracted and distinguishes between two states with the same
employment and hours worked but with different productivity. Hence, both
standard definitions are slightly biased and represent approximations to the
true productivity. Due to the absence of the true estimates of labor
productivity and related uncertainty in the approximating definitions we do not
put severe constraints on the precision in our modeling and seek only for a
visual fit between observed and predicted estimates.

In this study, we use the estimates of productivity and real GDP per
capita reported by the Conference Board (http://www.conference-board.org/economics/database.cfm).
Recently, we developed a model [3] describing
the evolution of labor force participation rate, LFP, in developed countries as a function of a single defining
variable – real GDP per capita. Natural fluctuations in real economic growth
unambiguously lead to relevant changes in labor force participation rate as expressed
by the following relationship:

{B1dLFP(t)/LFP(t) + C1}exp{ a1[LFP(t) - LFP(t0)]/LFP(t0) =

= ∫ {dG(t-T))/G(t-T) – A1/G(t-T)}dt(1)

where B1 and C1 are empirical (country-specific) calibration
constants, a1is empirical (also country-specific) exponent, t0is the start year of modeling,
T is the time lag, and dt=t2-t1, t1and t2are the start
and the end time of the time period for the integration of g(t) = dG(t-T))/G(t-T) – A1/G(t-T)
(one year in our model). Term A1/G(t-T),
where A1 is an empirical constant, represents the evolution
of economic trend. The exponential term defines the change in sensitivity to G due to the deviation of the LFP from its initial value LFP(t0). Relationship (1) fully
determines the behavior of LFP when G is an exogenous variable.

It follows
from (1) that labor productivity can be represented as a function of LFP and G, P~G∙Np/Np∙LFP = G/LFP,
where Np is the working age
population. Hence, P is a function of
G only. Therefore, the growth rate of
labor productivity can be represented using several independent variables.
Because the change in productivity is synchronized with that in G and labor force participation, first
useful form mimics (1):

where B2 and C2 are empirical calibration constants. Inherently, the
participation rate is not the driving force of productivity, but (1′)
demonstrates an important feature of the link between P and LFP – the same
change in the participation rate may result in different changes in the
productivity depending on the level of the LFP.

In order to
obtain a simple functional dependence between P and G one can use two
alternative forms of (1), as proposed in [1]:

{B3dLFP(t)/LFP(t) + C3} exp{a2[LFP(t) - LFP(t0)]/LFP(t0)} = Ns(t-T)

dP(t)/P(t)= B4Ns(t-T)+
C4(2)

where Ns is the number of S-year-olds, i.e. in the specific age
population, B3,…, C4 are empirical constant
different from B2, C2, anda2=a1. In this representation,
weuse our finding that the evolution of
real GDP per capita is driven by the change rate of the number of S-year-olds. Relationship (2) links dP/P and Ns directly.

The
following relationship defines dP/P
as a nonlinear function of G only:

N(t2) = N(t1)·{ 2[dG(t2-T)/G(t2-T)
- A2/G(t2-T)] + 1}(3)

dP(t2)/P(t2) = N(t2-T)/B
+ C(4)

where N(t) is the (formally defined) specific
age population, as obtained using A2
instead of A1; B and C are empirical constants. Relationship (3) defines the evolution
of some specific age population, which is different from actual one.

Productivity
prediction

Here we revisit the case
of Belgium using 5 new readings (between 2007 and 2012). For the prediction, we
use the previously obtained model [2] as described in Figure 3. Figure 3’
displays the measured and predicted rate of productivity growth. The curves are
very close with R2=0.82 for the period between 1967 and 2012. For Belgium,
the range of productivity change varies from 0.05 y-1 in the 1970s
to -0.03 y-1 in 2008 and 2009. As predicted in our previous paper ,
P was rather negative after 2007.

The current rate of productivity
growth is close to 0.0 y-1. The
case of Belgium is characterized by a 5-year lag of the productivity reaction
to the change in GDP. Therefore, we can predict the evolution of dP/P five
years ahead. Figure 3’ shows that the rate of growth in labor productivity will
be positive after 2013. This is a good news.

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